CN103346565A - Method for identifying weak nodes of power grid based on vector digraph - Google Patents

Abstract

The invention discloses a method for identifying weak nodes of a power grid based on a vector digraph in the technical field of voltage stability control of power systems. The method comprises the following steps of: determining first-level characteristic attributes of load nodes and second-level characteristic attributes respectively corresponding to the first-level characteristic attributes; acquiring values of the characteristic attributes of the load nodes; extracting main characteristic attributes of the load nodes; calculating weights of the main characteristic attributes of the load nodes; generating the vector digraphs of the load nodes according to the weight of the main characteristic attribute of each load node; calculating vector quantized values of the load nodes according to the vector digraphs of the load nodes; and determining the weak nodes of the power grid according to the vector quantized values of the load nodes. According to the method, the comprehensive attributes of the load nodes are considered, the mutual relation among the load nodes is established through the vector digraphs, and the weak nodes of the power grid are further determined, so that the efficient support is provided for the voltage stability control.

Description

Electrical network weak node discrimination method based on vectorial directed graph

Technical field

The invention belongs to power system voltage stabilization control technology field, relate in particular to a kind of electrical network weak node discrimination method based on vectorial directed graph.

Background technology

Along with the continuous expansion of the interconnected scale of electric power system, the electricity needs sharp increase, transregional remarkable day by day with the feature long distance power transmission system.In recent years, a lot of voltage collapse accidents take place both at home and abroad, cause great economic loss, find by analyzing these power system accidents: electrical network generation voltage unstability is the node a little less than some or certain several voltage stability relative thin often, and then feed through to other nodes, finally cause the collapse of whole system voltage.In electric power system, different load buses has different influence degrees to voltage stability.By selecting appropriate nodes to carry out reactive power compensation, can improve the voltage stability of electric power system to a great extent.Here " appropriate nodes " said in fact is exactly the weak load bus of relative thin in the electrical network.By determining load bus weak in the electrical network, realize the reactive power compensation to these nodes, and then guarantee the stability of power system voltage.Therefore, accurately weak load bus in the identification electrical network provides guidance to become present problem demanding prompt solution for line voltage is stable.

The method that weak node in some identification electrical networks is arranged in the prior art.Such as, application number is 201110091237.1, name is called the Chinese invention patent (publication No.: CN102157938A) of " power system voltage stabilization weak node ONLINE RECOGNITION method ", a kind of weak node ONLINE RECOGNITION method is provided, the node measurement information of discontinuity surface is realized the identification that voltage is stablized weak node when utilizing network impedance data and list.For another example, name is called the article (" electric power network technique " of " the multiple criteria integrated voltage stability index of identification electrical network weak node ", the 26-31 page or leaf, Gao Peng, Shi Libao, Yao Liangzhong, Ni Yixin, Masoud Bazargan, the 33rd the 19th phase of volume, in November, 2009), a kind of discrimination method of electrical network weak node has been proposed, this method adopts the ideal point evaluation method that two kinds of indexs are carried out comprehensively being used for the identification weak node according to change in voltage index and the reactive power nargin of P-V and Q-V curve calculation egress again.The method of other identification weak nodes also is provided in the prior art certainly.But, there is a common problem in these methods, namely only consider the performance of node in system according to the individual attribute of node, do not consider the performance of node in system according to the synthesized attribute of node, more do not consider the relative performance of node from this angle of the correlation between node and the node.

The present invention proposes a kind of electrical network weak node discrimination method based on vectorial directed graph, consider the synthesized attribute of node, set up correlation between node and the node by vectorial directed graph, and then the relative performance of definite node in system, and obtain the weak node of electrical network thus.

Summary of the invention

The objective of the invention is to, a kind of electrical network weak node discrimination method based on vectorial directed graph is provided, be used for solving the problem that existing electrical network weak node recognition technology exists.

To achieve these goals, the technical scheme of the present invention's proposition is that a kind of electrical network weak node discrimination method based on vectorial directed graph is characterized in that described method comprises:

Step 1: determine the first order characteristic attribute of load bus and the second level characteristic attribute of each first order characteristic attribute correspondence;

Step 2: obtain the value of the characteristic attribute of load bus, if namely the first order characteristic attribute of load bus has corresponding second level characteristic attribute, then obtain the value of second level characteristic attribute; If the first order characteristic attribute of load bus does not have corresponding second level characteristic attribute, then obtain the value of first order characteristic attribute;

Step 3: the main characteristic attribute that extracts load bus;

Step 4: the weights of the main characteristic attribute of calculated load node;

Step 5: according to the weights of the main characteristic attribute of each load bus, generate load bus vector directed graph;

Step 6: according to load bus vector directed graph calculated load knot vector quantized value;

Step 7: judge the electrical network weak node according to load bus vector quantization value.

The main characteristic attribute of described extraction load bus is that the value of the second level characteristic attribute that will set and characteristic attribute is greater than the union of the characteristic attribute of the set point main characteristic attribute as load bus.

The weights of the main characteristic attribute of described calculated load node are, the value of identical setting second level characteristic attribute adds up in the main characteristic attribute with each load bus, again divided by the minimum value in all accumulation results, as the weights of the second level characteristic attribute of setting in the main characteristic attribute; Be 1 with the weights assignment of the characteristic attribute beyond the second level characteristic attribute set in the main characteristic attribute.

Described step 5 specifically is, to any two load bus g _iAnd g _jIf, load bus g _iWith load bus g _jHave identical main characteristic attribute, and load bus g _iThe value of identical main characteristic attribute less than load bus g _jThe value of identical main characteristic attribute, then have one from load bus g _iPoint to load bus g _jAnd weights are the directed edge of the weights of this identical main characteristic attribute; Wherein, i ≠ j.

Described calculated load knot vector quantized value comprises:

Substep 101: provide each load bus g _iVector quantization value computing formula, described computing formula is

$v_i=(1-\mathrm{\&rho;})+\mathrm{\&rho;}\&times;(\frac{{\mathrm{<figure-callout\; id="1"\; label="n"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">n</figure-callout>}}_{1,i}}{c_1}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">n</figure-callout>}}_{2,i}}{c_2}{v}_2+...+\frac{n_{i-1,i}}{c_{i-1}}{v}_{i-1}+\frac{n_{i+1,i}}{c_{i+1}}{v}_{i+1}+...+\frac{n_{k,i}}{c_k}{v}_k);$

Wherein, v _iBe load bus g _iThe vector quantization value, i=1,2 ..., k;

ρ is the smoothing factor of setting, and ρ ∈ (0,1);

c _jBe load bus g _jPoint to other all load buses directed edge weights and, j=1,2 ..., k and j ≠ i;

n _{J, i}Be load bus g _jPoint to load bus g _iDirected edge weights and, j=1,2 ..., k and j ≠ i;

K is the number of load bus;

Substep 102: with the vector quantization value computing formula simultaneous of all load buses, obtain the vector quantization value system of linear equations of load bus, find the solution the vector quantization value that described system of linear equations obtains each load bus.

Described according to load bus vector quantization value judge the electrical network weak node be with all load buses according to the ascending rank order of its vector quantization value, sorting the preceding, load bus is the electrical network weak node.

The first order characteristic attribute of described load bus comprises the measurement baric flow of load bus, the power of load bus and the reactive power compensation amount of load bus;

The second level characteristic attribute of the measurement baric flow correspondence of described load bus comprises the measurement voltage of load bus and the measurement electric current of load bus;

The second level characteristic attribute of the power correspondence of described load bus comprises the active power of load bus, the reactive power of load bus and the power-factor angle of load bus.

The present invention considers the synthesized attribute of load bus, sets up correlation between load bus and the load bus by vectorial directed graph, and then the weak node of definite electrical network, can provide effective support for Voltage Stability Control.

Description of drawings

Fig. 1 is based on the electrical network weak node discrimination method flow chart of vectorial directed graph;

Fig. 2 is that the load bus vector directed graph that embodiment provides generates schematic diagram.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit the scope of the invention and to use.

Embodiment

Fig. 1 is based on the electrical network weak node discrimination method flow chart of vectorial directed graph, and as shown in Figure 1, the electrical network weak node discrimination method based on vectorial directed graph provided by the invention comprises:

Step 1: determine the first order characteristic attribute of load bus and the second level characteristic attribute of each first order characteristic attribute correspondence.

In the present embodiment, the first order characteristic attribute of load bus is the measurement baric flow r of load bus ₁, load bus power r ₂Reactive power compensation amount r with load bus ₃The second level characteristic attribute of the measurement baric flow correspondence of load bus comprises the measurement voltage r of load bus ₁₁Measurement electric current r with load bus ₁₂The second level characteristic attribute of the power correspondence of load bus comprises the active power r of load bus ₂₁, load bus reactive power r ₂₂Power-factor angle r with load bus ₂₃The reactive power compensation amount r of load bus ₃There is not second level characteristic attribute.

Step 2: obtain the value of the characteristic attribute of load bus, if namely the first order characteristic attribute of load bus has corresponding second level characteristic attribute, then obtain the value of second level characteristic attribute; If the first order characteristic attribute of load bus does not have corresponding second level characteristic attribute, then obtain the value of first order characteristic attribute.

In the present embodiment, actual is exactly the measurement voltage r that obtains each load bus respectively ₁₁, measure electric current r ₁₂, active power r ₂₁, reactive power r ₂₂, power-factor angle r ₂₃With reactive power compensation amount r ₃Value.These values can be obtained from the existing EMS system of electric power system or SCADA system.Present embodiment has been selected 3 load bus g at random ₁, g ₂And g ₃, the characteristic attribute value of each load bus adopts { r ₁₁, r ₁₂, r ₂₁, r ₂₂, r ₂₃, r ₃Form represents, then the value of the characteristic attribute that obtains of three load buses is respectively: g ₁={ 0.9,0.9,0.7,0.6,0.3,0.7}, g ₂={ 0.6,0.3,0.4,0.8,0.55,0.4}, g ₃={ 0.85,0.2,0.65,0.7,0.4,0.8}.

Step 3: the main characteristic attribute that extracts load bus.

Main characteristic attribute is actually the set of some characteristic attribute, in the present invention, defines main characteristic attribute and be the value of the second level characteristic attribute set and characteristic attribute greater than the union of the characteristic attribute of set point.

In the present embodiment, order measures voltage r ₁₁, active power r ₂₁With reactive power r ₂₂Be the second level characteristic attribute of setting, namely the second level characteristic attribute of She Dinging is { r ₁₁, r ₂₁, r ₂₂.Getting set point is 0.5, for load bus g ₁, the value of characteristic attribute is { r greater than 0.5 characteristic attribute ₁₁, r ₁₂, r ₂₁, r ₂₂, r ₃, load bus g then ₁Main characteristic attribute be { r ₁₁, r ₂₁, r ₂₂∪ { r ₁₁, r ₁₂, r ₂₁, r ₂₂, r ₃}={ r ₁₁, r ₁₂, r ₂₁, r ₂₂, r ₃.Similarly, for load bus g ₂, the value of characteristic attribute is { r greater than 0.5 characteristic attribute ₁₁, r ₂₂, r ₂₃, load bus g then ₂Main characteristic attribute be { r ₁₁, r ₂₁, r ₂₂∪ { r ₁₁, r ₂₂, r ₂₃}={ r ₁₁, r ₂₁, r ₂₂, r ₂₃.In like manner can get load bus g ₃Main characteristic attribute be { r ₁₁, r ₂₁, r ₂₂, r ₂₃, r ₃.

Step 4: the weights of the main characteristic attribute of calculated load node.

The weights of the main characteristic attribute of calculated load node are exactly that the value of setting second level characteristic attribute identical in the main characteristic attribute with each load bus adds up, again divided by the minimum value in all accumulation results, as the weights of the second level characteristic attribute of setting in the main characteristic attribute; Be 1 with the weights assignment of the characteristic attribute beyond the second level characteristic attribute set in the main characteristic attribute.

At present embodiment, the second level characteristic attribute of setting is { r ₁₁, r ₂₁, r ₂₂.The value of identical setting second level characteristic attribute adds up in the main characteristic attribute with each load bus, in fact is exactly with load bus g ₁, g ₂And g ₃Second level characteristic attribute be r ₁₁Value add up, the accumulation result that obtains is 2.35.With load bus g ₁, g ₂And g ₃Second level characteristic attribute be r ₂₁Value add up, the accumulation result that obtains is 1.75.With load bus g ₁, g ₂And g ₃Second level characteristic attribute be r ₂₂Value add up, the accumulation result that obtains is 2.1.In the above-mentioned accumulation result 2.35,1.75 and 2.1, what value was minimum is 1.75.Therefore, with 2.35/1.75=1.34,1.75/1.75=1 and 2.1/1.75=1.2 respectively as second level characteristic attribute r ₁₁, r ₂₁And r ₂₂Weights.Main characteristic attribute for other is made as 1 with weights, i.e. main characteristic attribute r ₂₃And r ₃Weights be 1.

Step 5: according to the weights of the main characteristic attribute of each load bus, generate load bus vector directed graph.

What generate vectorial directed graph specifically is, to any two load bus g _iAnd g _jIf, g _iAnd g _jHave identical main characteristic attribute, and load bus g _iThe value of identical main characteristic attribute less than load bus g _jThe value of identical main characteristic attribute, then have one from g _iPoint to g _jAnd weights are the directed edge of the weights of this identical main characteristic attribute; Wherein, i ≠ j.

As shown in Figure 2, in the present embodiment, load bus g ₁With load bus g ₂Has identical main characteristic attribute r ₁₁, r ₂₁And r ₂₂, load bus g ₂Main characteristic attribute r ₁₁Value be 0.6, less than load bus g ₁Main characteristic attribute r ₁₁Value 0.9, then from g ₂Point to g ₁A directed edge is arranged, and the weights of this directed edge are main characteristic attribute r ₁₁Weights, namely 1.34.In like manner, load bus g ₂Main characteristic attribute r ₂₁Value be 0.4, less than load bus g ₁Main characteristic attribute r ₂₁Value 0.7, then from g ₂Point to g ₁A directed edge is arranged, and the weights of this directed edge are main characteristic attribute r ₂₁Weights, namely 1.In Fig. 2, from g ₂Point to g ₁Two directed edges adopt numerical value to add and mode show, namely among Fig. 2, from g ₂Point to g ₁Numerical value " 1.34+1 " expression of directed edge from g ₂Point to g ₁Comprise two directed edges, the weights of a directed edge are that the weights of 1.34, one directed edges are 1.It is identical therewith with the implication of expression that among Fig. 2 other add.Load bus g ₁Main characteristic attribute r ₂₂Value be 0.6, less than load bus g ₂Main characteristic attribute r ₂₂Value 0.8, then from g ₁Point to g ₂A directed edge is arranged, and the weights of this directed edge are main characteristic attribute r ₂₂Weights, namely 1.2.

According to the method, with load bus g ₁With load bus g ₃And with load bus g ₂With load bus g ₃Between directed edge draw, and the weights of every directed edge correspondence are marked, finally generate load bus vector directed graph.

Step 6: according to load bus vector directed graph calculated load knot vector quantized value.

Calculated load knot vector quantized value comprises:

Substep 101: provide each load bus g _iVector quantization value computing formula as follows:

$v_i=(1-\mathrm{\&rho;})+\mathrm{\&rho;}\&times;(\frac{{\mathrm{<figure-callout\; id="1"\; label="n"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">n</figure-callout>}}_{1,i}}{c_1}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">n</figure-callout>}}_{2,i}}{c_2}{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">v</figure-callout>}}_2+...+\frac{n_{i-1,i}}{c_{i-1}}{v}_{i-1}+\frac{n_{i+1,i}}{c_{i+1}}{v}_{i+1}+...+\frac{n_{k,i}}{c_k}{v}_k)---\left(1\right)$

In the formula (1), v _iBe load bus g _iThe vector quantization value, i=1,2 ..., k.ρ is the smoothing factor of setting, and ρ ∈ (0,1).c _jBe load bus g _jPoint to other all load buses directed edge weights and, j=1,2 ..., k and j ≠ i.n _{J, i}Be load bus g _jPoint to load bus g _iDirected edge weights and, j=1,2 ..., k and j ≠ i.K is the number of load bus.、

In an embodiment, set smoothly because ρ=0.5.Simultaneously, can get c according to Fig. 2 ₁=1.2+1.2+1=3.4, c ₂=1.34+1+1.34+1=4.68, c ₃=1.34+1+1.2=3.54.n _1,2=1.2，n _1,3=1.2+1=2.2，n _2,1=1.34+1=2.34，n _2,3=1.34+1=2.34，n _3,1=1.34+1=2.34，n _3,2=1.2。

Can draw following 3 equations by above-mentioned formula (1):

$v_i=(1-0.5)+0.5\&times;(\frac{{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">n</figure-callout>}}_{2,1}}{c_2}{v}_2+\frac{{\mathrm{<figure-callout\; id="3"\; label="n"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">n</figure-callout>}}_{3,1}}{c_3}{v}_3)=0.5+0.5\&times;(\frac{2.34}{4.68}{v}_2+\frac{2.34}{3.54}{v}_3)---\left(2\right)$

$v_2=(1-0.5)+0.5\&times;(\frac{{\mathrm{<figure-callout\; id="1"\; label="n"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">n</figure-callout>}}_{1,2}}{c_1}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{{\mathrm{<figure-callout\; id="3"\; label="n"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">n</figure-callout>}}_{3,2}}{c_3}{v}_3)=0.5+0.5\&times;(\frac{1.2}{3.4}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{1.2}{3.54}{v}_3)---\left(3\right)$

${\mathrm{<figure-callout\; id="3"\; label="v"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">v</figure-callout>}}_3=(1-0.5)+0.5\&times;(\frac{{\mathrm{<figure-callout\; id="1"\; label="n"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">n</figure-callout>}}_{\mathrm{1,3}}}{c_1}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">n</figure-callout>}}_{\mathrm{2,3}}}{c_2}{v}_2)=0.5+0.5\&times;(\frac{2.2}{3.4}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{2.34}{4.68}{v}_2)---\left(4\right)$

Substep 102: with the vector quantization value computing formula simultaneous of all load buses, obtain the vector quantization value system of linear equations of load bus, find the solution the vector quantization value that described system of linear equations obtains each load bus.

In the present embodiment, with formula (2)-(4) simultaneous, obtain following equation group:

$\left\{\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1=0.5+0.5\&times;(\frac{2.34}{4.68}{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">v</figure-callout>}}_2+\frac{2.34}{3.54}{v}_3)\\ {\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">v</figure-callout>}}_2=0.5+0.5\&times;(\frac{1.2}{3.4}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{1.2}{3.54}{v}_3)\\ {\mathrm{<figure-callout\; id="3"\; label="v"\; filenames="HDA00003576802200011.png"\; state="\{\{state\}\}">v</figure-callout>}}_3=0.5+0.5\&times;(\frac{2.2}{3.4}{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HDA00003576802200011.png,HDA00003576802200012.png"\; state="\{\{state\}\}">v</figure-callout>}}_1+\frac{2.34}{4.68}{v}_2)\end{array}\right.---\left(5\right)$

Equation group (5) is ternary once linear equation group, has unique solution.By solving equation group (5), can obtain load bus g _iVector quantization value v _i(i=1,2,3).The result who finds the solution is: load bus g ₁, vector quantization value v ₁=1.037, load bus g ₂Vector quantization value v ₂=0.899, load bus g ₃Vector quantization value v ₃=1.064

Step 7: judge the electrical network weak node according to load bus vector quantization value.

With load bus g ₁, g ₂And g ₃According to its vector quantization value v ₁, v ₂And v ₃Ascending rank order, its ranking results are { v ₂, v ₁, v ₃, hence one can see that load bus v ₂Be the weakest node in 3 load buses, load bus v ₁Be time weak node in 3 load buses, load bus v ₃It is the most stable node in 3 load buses.Therefore, when carrying out Voltage Stability Control, earlier the weakest node is started with from electrical network, and the weakest node is carried out reactive power compensation, and time weak node is started with from electrical network and then, and the node of inferior weakness is carried out reactive power compensation.Carry out successively, till the stabilization of power grids.In real work, often more than 3 of the load buses that need investigate, 3 nodes that present embodiment provides are just in order to illustrate implementation procedure of the present invention.With reference to said process, also can realize the identification of weak node more than the situation of 3 load buses.

The above; only for the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, and anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claim.

Claims (7)

1. electrical network weak node discrimination method based on vectorial directed graph is characterized in that described method comprises:

Step 1: determine the first order characteristic attribute of load bus and the second level characteristic attribute of each first order characteristic attribute correspondence;

Step 2: obtain the value of the characteristic attribute of load bus, if namely the first order characteristic attribute of load bus has corresponding second level characteristic attribute, then obtain the value of second level characteristic attribute; If the first order characteristic attribute of load bus does not have corresponding second level characteristic attribute, then obtain the value of first order characteristic attribute;

Step 3: the main characteristic attribute that extracts load bus;

Step 4: the weights of the main characteristic attribute of calculated load node;

Step 5: according to the weights of the main characteristic attribute of each load bus, generate load bus vector directed graph;

Step 6: according to load bus vector directed graph calculated load knot vector quantized value;

Step 7: judge the electrical network weak node according to load bus vector quantization value.

2. discrimination method according to claim 1, the main characteristic attribute that it is characterized in that described extraction load bus are that the value of the second level characteristic attribute that will set and characteristic attribute is greater than the union of the characteristic attribute of the set point main characteristic attribute as load bus.

3. discrimination method according to claim 2, the weights that it is characterized in that the main characteristic attribute of described calculated load node are, the value of identical setting second level characteristic attribute adds up in the main characteristic attribute with each load bus, again divided by the minimum value in all accumulation results, as the weights of the second level characteristic attribute of setting in the main characteristic attribute; Be 1 with the weights assignment of the characteristic attribute beyond the second level characteristic attribute set in the main characteristic attribute.

4. discrimination method according to claim 3 is characterized in that described step 5 specifically is, to any two load bus g _iAnd g _jIf, load bus g _iWith load bus g _jHave identical main characteristic attribute, and load bus g _iThe value of identical main characteristic attribute less than load bus g _jThe value of identical main characteristic attribute, then have one from load bus g _iPoint to load bus g _jAnd weights are the directed edge of the weights of this identical main characteristic attribute; Wherein, i ≠ j.

5. discrimination method according to claim 4 is characterized in that described calculated load knot vector quantized value comprises:

Substep 101: provide each load bus g _iVector quantization value computing formula, described computing formula is

$v_i=(1-\mathrm{\&rho;})+\mathrm{\&rho;}\&times;(\frac{n_{1,i}}{c_1}{v}_1+\frac{n_{2,i}}{c_2}{v}_2+...+\frac{n_{i-1,i}}{c_{i-1}}{v}_{i-1}+\frac{n_{i+1,i}}{c_{i+1}}{v}_{i+1}+...+\frac{n_{k,i}}{c_k}{v}_k);$

Wherein, v _iBe load bus g _iThe vector quantization value, i=1,2 ..., k;

ρ is the smoothing factor of setting, and ρ ∈ (0,1);

c _jBe load bus g _jPoint to other all load buses directed edge weights and, j=1,2 ..., k and j ≠ i;

n _{J, i}Be load bus g _jPoint to load bus g _iDirected edge weights and, j=1,2 ..., k and j ≠ i;

K is the number of load bus;

Substep 102: with the vector quantization value computing formula simultaneous of all load buses, obtain the vector quantization value system of linear equations of load bus, find the solution the vector quantization value that described system of linear equations obtains each load bus.

6. discrimination method according to claim 5, it is characterized in that described according to load bus vector quantization value judge the electrical network weak node be with all load buses according to the ascending rank order of its vector quantization value, sorting the preceding, load bus is the electrical network weak node.

7. according to any described discrimination method of claim among the claim 1-6, it is characterized in that the first order characteristic attribute of described load bus comprises the measurement baric flow of load bus, the power of load bus and the reactive power compensation amount of load bus;

The second level characteristic attribute of the measurement baric flow correspondence of described load bus comprises the measurement voltage of load bus and the measurement electric current of load bus;

The second level characteristic attribute of the power correspondence of described load bus comprises the active power of load bus, the reactive power of load bus and the power-factor angle of load bus.

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